Shape completion for 3-D point clouds is an important issue in the literature of computer graphics and computer vision. We propose an end-to-end shape-preserving point completion network through encoder–decoder architecture, which works directly on incomplete 3-D point clouds and can restore their overall shapes and fine-scale structures. To achieve this task, we design a novel encoder that encodes information from neighboring points in different orientations and scales, as well as a decoder that outputs dense and uniform complete point clouds. We augment a 3-D object dataset based on ModelNet40 and validate the effectiveness of our shape-preserving completion network. Experimental results demonstrate that the recovered point clouds lie close to ground truth points. Our method outperforms state-of-the-art approaches in terms of Chamfer distance (CD) error and earth mover’s distance (EMD) error. Furthermore, our end-to-end completion network is robust to model noise, the different levels of incomplete data, and can also generalize well to unseen objects and real-world data.

中文翻译：

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端到端保形点完成网络

3-D点云的形状完成是计算机图形学和计算机视觉文学中的重要问题。我们提出了一种通过编码器-解码器体系结构的端到端形状保持点完成网络，该网络可以直接在不完整的3D点云上工作，并且可以恢复其整体形状和精细的结构。为了实现此任务，我们设计了一种新颖的编码器，该编码器可对来自不同方向和比例的相邻点的信息进行编码，以及一种可输出密集且均匀的完整点云的解码器。我们基于ModelNet40扩充了3-D对象数据集，并验证了保形完成网络的有效性。实验结果表明，恢复的点云位于地面真点附近。在倒角距离（CD）误差和推土机距离（EMD）误差方面，我们的方法优于最新方法。此外，我们的端到端完成网络对建模噪声，不同级别的不完整数据具有强大的鲁棒性，并且还可以很好地推广到看不见的物体和真实世界的数据。